Bayesian and non-Bayesian evidential updating
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perspectives on the theory and practice of belief functions
International Journal of Approximate Reasoning
On the justification of Dempster's rule of combination
Artificial Intelligence
Evidence, knowledge, and belief functions
International Journal of Approximate Reasoning - Special issue: The belief functions revisited: questions and answers
The combination of belief: when and how fast?
International Journal of Approximate Reasoning - Special issue: The belief functions revisited: questions and answers
Updating with belief functions, ordinal conditional functions and possibility measures
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
The transferable belief model and other interpretations of Dempster-Shafer's model
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
A new approach to updating beliefs
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Experience-grounded semantics: a theory for intelligent systems
Cognitive Systems Research
Sequential weighted combination for unreliable evidence based on evidence variance
Decision Support Systems
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By analyzing the relationships among chance, weight of evidence and degree of belief, we show that the assertion "probability functions are special cases of belief functions" and the assertion "Dempster's rule can be used to combine belief functions based on distinct bodies of evidence" together lead to an inconsistency in Dempster-Shafer theory. To solve this problem, we must reject some fundamental postulates of the theory. We introduce a new approach for uncertainty management that shares many intuitive ideas with D-S theory, while avoiding this problem.